One of the main challenges in optimizing RecSys is correctly capturing user preferences from historical interaction data  [1]}, [2]}, [3]}, [4]}, [5]}.
A widely adopted approach is to train a model to predict known ratings and then apply it to previously unobserved user-item pairs  [6]}, [7]}, [8]}.
However, in the last decade it has become apparent that observed interactions are often a very skewed representation of the actual user preferences [9]}, [6]}, [11]}.
In particular, ratings are known to be affected by various forms of selection bias, such as popularity bias [9]}, positivity bias [6]} or position bias [14]}.
Popularity bias, for instance, occurs because as most interactions involve only a small number of very popular items, the patterns in observed ratings may not necessarily extrapolate to the data as a whole [11]}, [9]}, [6]}, [18]}.
It is now well understood that ignoring statistical biases during optimization is likely to lead to suboptimal recommendation performance [19]}, [18]}, [11]}
and numerous solutions have been proposed to address various forms of selection bias in recommendation data [18]}, [19]}, [24]}, [25]}, [26]}, [24]}, [28]}, [29]}, [30]}.
